GaborNet can be installed via pip from Python 3.7 and above:
pip install GaborNetimport torch
import torch.nn as nn
from torch.nn import functional as F
from GaborNet import GaborConv2d
# torch device should be specified explicitly when creating Gabor layer:
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
class GaborNN(nn.Module):
def __init__(self):
super(GaborNN, self).__init__()
self.g0 = GaborConv2d(in_channels=1, out_channels=96, kernel_size=(11, 11), device=device)
self.c1 = nn.Conv2d(96, 384, (3,3))
self.fc1 = nn.Linear(384*3*3, 64)
self.fc2 = nn.Linear(64, 2)
def forward(self, x):
x = F.leaky_relu(self.g0(x))
x = nn.MaxPool2d()(x)
x = F.leaky_relu(self.c1(x))
x = nn.MaxPool2d()(x)
x = x.view(-1, 384*3*3)
x = F.leaky_relu(self.fc1(x))
x = self.fc2(x)
return x
net = GaborNN().to(device)Original research paper (preprint): https://arxiv.org/abs/1904.13204
This research on deep convolutional neural networks proposes a modified architecture that focuses on improving convergence and reducing training complexity. The filters in the first layer of network are constrained to fit the Gabor function. The parameters of Gabor functions are learnable and updated by standard backpropagation techniques. The proposed architecture was tested on several datasets and outperformed the common convolutional networks
Please use this bibtex if you want to cite this repository in your publications:
@misc{gabornet,
author = {Alekseev, Andrey},
title = {GaborNet: Gabor filters with learnable parameters in deep convolutional
neural networks},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/iKintosh/GaborNet}},
}